Development of a Reference Energy Mean Emission Level Traffic Noise Models for Bituminous Pavement for Mid-Sized Cities in India. Saurabh Upadhyay 1 Department of Civil Engineering, Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India Manoranjan Parida 2 Department of Civil Engineering, Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India Brind Kumar 3 Department of Civil Engineering, Indian Institute of Technology (BHU) Varanasi Varanasi, Uttar Pradesh, India

ABSTRACT

Present study is aimed at the development of the Reference Energy Mean Emission Level (REMEL) of vehicle types involved in the mixed traffic flow on the bituminous pavements of Kanpur mid-sized city. REMEL is the basic input parameter for a traffic noise propagation model. Data were collected at five locations in Kanpur urban area under the conditions of free flow traffic, good surface and straight alignment. A total 10700 data sets for 11 vehicle categories (bus, truck, tractor-trailer, light commercial vehicle (LCV), 3-wheeler (auto, Vikram),car, motorcycle, e-rickshaw, bicycle, tricycle and horse driven vehicle) were collected from these locations under IMPRINT India sponsored research project funded by the Ministry of Education (MoE) and Ministry of Housing & Urban Affairs (MOHUA), Govt. of India. Sound Level (Leq) was measured using Type-1 Sound Level Meter (SLM) at a distance of 7.5 meters from the centre of the nearby carriageway for single vehicle pass by event. The vehicular cruising speed was also measured simultaneously. Regression analysis shows that the L eq has good correlation with the vehicular speed for every vehicle category. The study provides an interesting insight to noise emission characteristics of vehicle types for mixed traffic under Indian conditions.

1. INTRODUCTION

In rapidly developing urban areas of mid-sized cities, the problem of traffic noise pollution is

enormous, and it becomes complex[1]–[3]. The complexity arises from the migration of the

population from rural to urban areas of the cities, the construction of hundreds of kilometers of urban

1 Supadhyay1@ce.iitr.ac.in 2 M.Parida@ce.iitr.ac.in 3 kumar_brind.civ@iitbhu.ac.in

expressways, and an intense growth rate of households in socioeconomic activities. Studies have

shown that a significant portion of the city population is annoyed due to traffic noise [4]. For the

detailed analysis of noise impact and forecasts, FHWA Model has been commonly used. The model

can easily be calibrated for new conditions since the REMEL for different categories of vehicles are

operated as independent inputs to the model. FHWA has also defined and developed reference energy

mean emission levels as a function of vehicle category and vehicle speed [5]. Several research studies

in North America have shown that the use of the original REMEL published by FHWA may result in

a significant overestimation of noise levels in the vicinity of roadways where the studies were

performed [6]–[8]. However, only a few studies have included this problem for mid-sized Indian

cities and performed to examine and evaluate the transferability of the FHWA traffic noise models to

urban areas of the mid-sized Indian cities. Several related factors in the region vary from those of the

North American environment like poor vehicle maintenance practices, overloading of vehicles,

number of registered vehicles, use of horns, noisy silencers, and rough pavement surfaces caused by

poor material characteristics and lack of frequent and routine pavement maintenance.

The objective of this study was to develop a reference energy mean emission levels (REMEL)

model for 11 different vehicle categories on the basis of a large data sets (10700 data samples) for

Kanpur mid-sized city. These REMEL model may be used in the context of developing an FHWA

model for the Indian mid-sized city. This research was sponsored by the Ministry of Education (MoE)

and Ministry of Housing & Urban Affairs (MOHUA), Govt. of India under the IMPRINT India

sponsored project.

2. LITERATURE REVIEW

REMEL models proposed in the original FHWA model included three vehicle types –

automobiles, medium trucks and heavy trucks as shown in Equations (1) to (3).

Automobile (A): 𝐿 0𝐴 = 38.1 log(𝑆) −2.4 (1) Medium trucks (MT): 𝐿 0𝑀𝑇 = 33.9 log(𝑆) + 16.4 (2) Heavy trucks (HT): 𝐿 0𝐻𝑇 = 24.6 log(𝑆) + 38.5 (3) The above models are based on segregated traffic flow. The mid-sized Indian cities experience mixed

traffic flow. REMEL models of Thailand may be close to the Indian situation as shown in Equations

(4) to (10).

Automobile: 𝐿 𝑒𝑞 = 63.07 + 0.07𝑆 (𝑅 2 = 0.432) (4)

Light truck: 𝐿 𝑒𝑞 = 63.78 + 0.12𝑆 (𝑅 2 = 0.525) (5)

Medium truck: 𝐿 𝑒𝑞 = 72.57 −0.01𝑆 (𝑅 2 = 0.298) (6)

Heavy truck: 𝐿 𝑒𝑞 = 72.35 + 0.07𝑆 (𝑅 2 = 0.337) (7)

Motorcycle: 𝐿 𝑒𝑞 = 65.93 + 0.12𝑆 (𝑅 2 = 0.238) (8)

Bus: 𝐿 𝑒𝑞 = 68.18 + 0.10𝑆 (𝑅 2 = 0.546) (9)

Semi and full-trailer: 𝐿 𝑒𝑞 = 67.09 + 0.14𝑆 (𝑅 2 = 0.325) (10)

3. STUDY AREA AND DATA COLLECTION

Kanpur is located at 26.449923 latitudes and 80.331871 longitude in the Indian state of Uttar

Pradesh. It is among the largest industrial centre of northern India having population of 4.58 million

and an area of 3,155 sq.km. The total registered vehicle is 1.61 million

(Source: http://kmc.up.nic.in/Introduction.htm , & https://vahan.parivahan.gov.in/vahan4dashboard/ ) .

The data was collected from five locations - Neori, Golf Course, Berry Kalyanpur, Armapur Estate

Residential Area, and New Transport Nagar after ensuring free flow traffic, level and good surface,

and straight alignment as shown in Table 1. Neori was located on a National Highway, Golf Course

and New Transport Nagar were on a collector street, Berry Kalyanpur and Armapur Estate were on

an arterial/sub-arterial road.

Table 1. Location details for Reference Energy Mean Emission Level Study in Kanpur

Sr.

Type of

Latitude

Location Name Land Use

No.

Pavement

and Longitude

Residential and New

26°31'6.33"N &

1. Berry, Kalyanpur

Bituminous

Developed area

80°15'30.10"E

Golf Course,

26°26'39.33"N &

2.

Residential and Silent Bituminous

Kanpur Cantonment

80°23'23.37"E

26°21'47.32"N &

3. Neori, Kanpur Commercial Bituminous

80°18'16.08"E

Residential and

26°25'28.49"N &

4. Kidwai Nagar

Bituminous

Commercial

80°20'1.34"E

26°27'50.36"N &

5. Armapur Estate Residential and Silent Bituminous

80°15'57.97"E

4. MATERIAL AND METHODOLOGY

The sound levels were measured using a Type-1 Bruel & Kjaer (B&K) microphone incorporating

Sound Level Meter (SLM) with frequency weighting as "A". The cruising speed of vehicles were

measured using a Falcon HR K-band handheld radar speed gun which works on a frequency of K-

Band 24.125 GHz ± 100 MHz and capable of measuring speeds in the range of 9 to 334 km/hr. For

slow-moving vehicle, the speeds were measured by determining time over a distance of 30m.

Table 2. Details of Vehicle Category and Number of Samples

Types of Vehicle Sample size Types of Vehicle Sample size

Bus 305 Motorcycle 3515

Truck 862 E-Rickshaw 497

Tractor-trailer 270 Bicycle 1029

Tri-cycle

Light Commercial Vehicle (LCV) 575

88

(Cycle rickshaw)

Car 2261

Horse Driven Vehicle 23 Three-Wheeler, Auto

426

Vikram

849

(a) Berry, Kalyanpur (b) Neori

Figure 1. Site Photograph of REMEL Locations (a) Berry, Kalyanpur (b) Neori

The study was carried out in compliance with FHWA procedure [15] which stipulates the selected

locations to be level and free of extraneous terrain effects; microphone of SLM to be 1.2 meters above

the pavement surface and 7.5 meters from the centerline of the nearest carriageway; a clear line of

sight to the roadway and a 150-degree unobscured arc; roads gradient of less than 2% and roadway

surface to be dry, smooth asphalt or concrete. Noise from other vehicles could contaminate each

sample; thus, measurement locations were chosen carefully. This was accomplished by selecting

areas that were wide, unobstructed, or had low traffic volumes. The observer kept a close eye on the

sound level meter while physically listening for interference from other vehicles. The careful

implementation of this approach ensured that noise from other sources did not taint the emission level

samples. Figure 1 shows the work being conducted at the study locations of New Transport Nagar

and Neori.

Wind speed, temperature, and humidity were checked at a local Meteorological Station. It was

assumed that humidity and temperature did not vary significantly at the measurement sites.

Measurements were halted if the wind began gusting or the constant wind speed was suspected to be

approaching 5 km/hr. The SLM was calibrated before field use, and the radar speed gun was

calibrated before and after each measurement session.

The Road traffic of Kanpur was categorized into eleven groups: bus, truck, tractor-trailer, light

commercial vehicle (LCV), car, 3-wheeler (auto, Vikram), motorcycle, e-rickshaw, bicycle, tricycle

(cycle rickshaw) and horse driven vehicle. The details of vehicle category and the sample size are

shown in Table 2. The L eq data were grouped into a speed range ±2 km/hr to ±5 km/hr. The mean L eq

was calculated to represent the noise level of the speed class. The average speed of the class was also

Bus aaaeze (lap asion * <0 ‘Speed (Km/hr) 30 2»

obtained and a regression equation with speed as independent parameter was obtained for prediction

of L eq .

5. DEVELOPMENT OF REMEL EQUATIONS

Equation (11) was used to obtain the mean L eq for each class of speed.

𝐿 𝑖𝑡ℎ

1

10 𝑛 𝑖=1 ] (11)

𝐿 𝑒𝑞.,𝑚𝑒𝑎𝑛 = 10 𝑙𝑜𝑔[

𝑛 ∑ 10

Figure 2 shows the scatter and relationships between L eq and vehicle speed for various vehicle

categories like bus, truck, tractor-trailer, light commercial vehicle (LCV), car, 3-wheeler (auto,

Vikram), motorcycle, e-rickshaw, bicycle, tricycle (cycle rickshaw) and horse-driven vehicles. The

equation followed by these relationships were identified as the REMEL for the respective vehicle

dB(A) 20 euo Truck Speed (km/hr)

category which are shown in Table 3.

Lg Noise dB(A) Tractor e30 250 og nol}, Speed (Km/hr)

Lg Noise dB(A) 786 782 Light Commercial Vehicle (LCV) ‘Speed (Km/hr)

(vlap 2sion®™ ‘Speed (km/hr)

‘Auto iy aes (wha aston ®™ & 7% 0 30 2 10 Speed (Km/he)

1aNoise dB(A) Oversized 3-W (Vikram) ‘Speed (Km/hr)

z g 2 Motorcycle 10 20 30 «0 ar) Speed (Km/h) ” 0 100

Lg Noise dB(A) no E-Rickshaw 20 30 ° ‘Speed (km/hr)

aq Noise dB(A) ” Bicycle ‘Speed (Km/hr)

Noise dB(A) Speed (Km/hr)

Figure 2. Relationship between L eq and speed for different vehicle categories

Lag Noise d0(A) Horse Driven Vehicle now ow Speed (Km/hr 20

Table 3. REMEL Equations for different Vehicle Category

Reference Energy Mean Emission

R 2 Value

Sr. No. Vehicle Category

Level (REMEL) Equations

1. Bus (L 0 ) E Bus = 5.3194 log(x) + 58.581 R² = 0.93

2. Truck (L 0 ) E Truck = 3.8598 log(x) + 67.862 R² = 0.71

3. Tractor-trailer (L 0 ) E TT = 4.1957 log(x) + 69.12 R² = 0.81

Light Commercial Vehicle

4.

(L 0 ) E LCV = 1.227 log(x) + 71.259 R² = 0.87

(LCV)

5. Car (L 0 ) E Car = 5.9198 log(x) + 49.13 R² = 0.94

Three-Wheeler

(L 0 ) E Auto = 6.6582 log(x) + 50.718

R² = 0.87

6.

(Auto, Vikram)

(L 0 ) E Vikram = 5.1794 log(x) + 61.576

R² = 0.92

7. Motorcycle (L 0 ) E MC = 8.0902 log(x) + 41.457 R² = 0.95

8. E-Rickshaw (L 0 ) E e-Rick = 7.3559 log(x) + 45.529 R² = 0.96

9. Bicycle (L 0 ) E Bic = 2.9004 log(x) + 46.191 R² = 0.94

10. Tri-cycle (Cycle rickshaw) (L 0 ) E TC = 8.5182 log(x) + 34.63 R² = 0.91

11. Horse Driven Vehicle (L 0 ) E HDV = 32.679 log(x) - 23.165 R² = 0.84

Where ' x' is speed in km/hr.

6. CONCLUSION

The present study represents a first of its kind work in determining REMEL for various vehicle

categories under mixed traffic flow regime. As road design aids and for assessing existing or predicted

changes in traffic noise circumstances, a reference energy mean emission level or basic noise level is

necessary to develop the traffic noise prediction model. These REMEL equations also help to

estimate the basic noise level of that area or location. REMEL equations have been established for

11 vehicle categories viz. bus, truck, tractor-trailer, light commercial vehicle (LCV), car, 3-wheeler

(auto, Vikram), motorcycle, e-rickshaw, bicycle, tricycle (cycle rickshaw) and horse-driven vehicles

in 5 locations in Kanpur with vehicular speed as independent parameter.

7. ACKNOWLEDGMENT

Authors wish to acknowledge Ministry of Education (MoE) and Ministry of Housing & Urban

Affairs (MOHUA) Govt. of India, under the flagship programme of IMPRINT India for providing

financial support. We also acknowledge the Ministry of Education Doctoral Fellowship to Mr.

Saurabh Upadhyay which has been utilized for pursuing this research.

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